Web search engine logs are widely used for optimizing search engine performance. In particular, analyzing clickthrough data in the logs, which indicate when a search result has been clicked, is a significant part of search engine optimization. Clickthrough data is used as an indicator as to which documents users found as being relevant to a query.
However, interpreting clickthrough data for use in optimizing search engines is far from ideal, as the mere fact that a URL has been clicked is not necessarily a good indicator of its relevance to the query. Notwithstanding, existing optimization mechanisms tend to treat clickthrough data as a strong indicator. For example, some mechanisms that exploit clickthrough data assume that the user examined each document's information (e.g., title, snippet and so on) returned in a ranked list of search results, and clicked on the relevant documents. This is not what users do in many instances.
Other mechanisms assume that perceived relevance (i.e., attractiveness) is the same as actual relevance, or assume that all clicks provided the user with some satisfaction with respect to gaining useful information. Again, this not the case, as perceived relevance is often different from actual relevance, and many users are frequently unsatisfied with the search results returned for a query.